Skin regeneration,repair, and reconstruction: present and future

European Surgery - Large skin defects caused by trauma (e.g., burns) or due to other reasons (e.g., tumor-related skin resections) require sufficient skin replacement. The constant improvement of...     

Praxis der perioperativen Prävention von Phantomschmerz: eine deutschlandweite Umfrage

Die Anaesthesiologie - Phantomschmerzen haben eine hohe Prävalenz nach Majoramputationen und sind mit einer zusätzlichen Einschränkung der Lebensqualität verbunden....     

人工智能在中医药领域的应用进展及现状思考

人工智能（Artificial Intelligence，AI）的蓬勃兴起为现代社会带来了前所未有的机遇，中医药是中华民族传承千年的文化瑰宝。随着人工智能技术不断在中医药领域的科技创新中崭露头角，二者的融合不断加深，人工智能在中医药领域的发展前景、争议挑战也引发了诸多思考。本文将从人工智能在中医药领域的应用入手，对人工智能辅助中医诊断、智能决策与数据挖掘、健康管理及中草药现代化研究等方面，就近年来国内外研究进展进行总结与分析，以期为AI视域下实现中医药现代化、智能化赋能。     

PREDICT-GTN 1: Can we improve the FIGO scoring system in gestational trophoblastic neoplasia?

Gestational trophoblastic neoplasia (GTN) patients are treated according to the eight-variable International Federation of Gynaecology and Obstetrics (FIGO) scoring system, that aims to predict first-line single-agent chemotherapy resistance. FIGO is imperfect with one-third of low-risk patients developing disease resistance to first-line single-agent chemotherapy. We aimed to generate simplified models that improve upon FIGO. Logistic regression (LR) and multilayer perceptron (MLP) modelling (n = 4191) generated six models (M1-6). M1, all eight FIGO variables (scored data); M2, all eight FIGO variables (scored and raw data); M3, nonimaging variables (scored data); M4, nonimaging variables (scored and raw data); M5, imaging variables (scored data); and M6, pretreatment hCG (raw data) + imaging variables (scored data). Performance was compared to FIGO using true and false positive rates, positive and negative predictive values, diagnostic odds ratio, receiver operating characteristic (ROC) curves, Bland-Altman calibration plots, decision curve analysis and contingency tables. M1-6 were calibrated and outperformed FIGO on true positive rate and positive predictive value. Using LR and MLP, M1, M2 and M4 generated small improvements to the ROC curve and decision curve analysis. M3, M5 and M6 matched FIGO or performed less well. Compared to FIGO, most (excluding LR M4 and MLP M5) had significant discordance in patient classification (McNemar's test P < .05); 55-112 undertreated, 46-206 overtreated. Statistical modelling yielded only small gains over FIGO performance, arising through recategorisation of treatment-resistant patients, with a significant proportion of under/overtreatment as the available data have been used a priori to allocate primary chemotherapy. Streamlining FIGO should now be the focus.     

New frontiers in applied veterinary point‐of‐capture diagnostics: Toward early detection and control of zoonotic influenza

Among the chief limitations in achieving early detection and control of animal‐origin influenza of pandemic potential in high‐risk livestock populations is the existing lag time between sample collection and diagnostic result. Advances in molecular diagnostics are permitting deployment of affordable, rapid, highly sensitive, and specific point‐of‐capture assays, providing opportunities for targeted surveillance driving containment strategies with potentially compelling returns on investment. Interrupting disease transmission at source holds promise of disrupting cycles of animal‐origin influenza incursion to endemicity and limiting impact on animal production, food security, and public health. Adoption of new point‐of‐capture diagnostics should be undertaken in the context of promoting robust veterinary services systems and parallel support for operationalizing pre‐authorized plans and communication strategies that will ensure that the full potential of these new platforms is realized.